


import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE			: List[Any]				   =					logging.getLogger()



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				Tuple 						=						argparse.ArgumentParser()
    parser.add_argument('-f'					)
    _lowercase			:				int 						=						parser.parse_args()
    return args.f



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Dict:
    _lowercase			:				List[str] 						=						{}
    _lowercase			:				int 						=						os.path.join(lowerCamelCase_				,					'all_results.json'					)
    if os.path.exists(lowerCamelCase_					):
        with open(lowerCamelCase_				,					'r'					) as f:
            _lowercase			:				Any 						=						json.load(lowerCamelCase_					)
    else:
        raise ValueError(F'''can\'t find {path}'''					)
    return results



def 							UpperCamelCase_( )       ->		Tuple:
    _lowercase			:				Optional[int] 						=						torch.cuda.is_available() and torch_device == 'cuda'
    return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE			: Optional[int]				   =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class _lowerCamelCase(		_a  ):
       @classmethod
       def      UpperCamelCase	(   cls)			->  Tuple:




           """simple docstring"""

           _lowercase			:				str 						=						tempfile.mkdtemp()
           _lowercase			:				Union[str, Any] 						=						os.path.join(cls.tmpdir, 'default_config.yml')
           write_basic_config(save_location=cls.configPath)
           _lowercase			:				int 						=						['accelerate', 'launch', '--config_file', cls.configPath]
       @classmethod
       def      UpperCamelCase	(   cls)			->  Optional[int]:




           """simple docstring"""

           shutil.rmtree(cls.tmpdir)
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				List[str] 						=						F'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           if is_cuda_and_apex_available():
               testargs.append('--fp16')

           run_command(self._launch_args + testargs)
           _lowercase			:				Union[str, Any] 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_accuracy'], 0.7_5)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'glue_no_trainer')))
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Optional[int] 						=						F'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           if torch.cuda.device_count() > 1:
               # Skipping because there are not enough batches to train the model + would need a drop_last to work.
               return

           run_command(self._launch_args + testargs)
           _lowercase			:				List[str] 						=						get_results(lowerCamelCase)
           self.assertLess(result['perplexity'], 1_00)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'clm_no_trainer')))
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Optional[int] 						=						F'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				str 						=						get_results(lowerCamelCase)
           self.assertLess(result['perplexity'], 42)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'mlm_no_trainer')))
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				str 						=						7 if get_gpu_count() > 1 else 2

           _lowercase			:				Any 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				str 						=						F'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				Optional[int] 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_accuracy'], 0.7_5)
           self.assertLess(result['train_loss'], 0.5)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'ner_no_trainer')))
       @unittest.skip(reason='Fix me @muellerzr')
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				str 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Tuple 						=						F'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				Optional[Any] 						=						get_results(lowerCamelCase)
           # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
           self.assertGreaterEqual(result['eval_f1'], 28)
           self.assertGreaterEqual(result['eval_exact'], 28)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'qa_no_trainer')))
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				str 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				List[str] 						=						F'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				int 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_accuracy'], 0.8)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'swag_no_trainer')))
       @slow
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Any 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				str 						=						F'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				Optional[int] 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_rouge1'], 10)
           self.assertGreaterEqual(result['eval_rouge2'], 2)
           self.assertGreaterEqual(result['eval_rougeL'], 7)
           self.assertGreaterEqual(result['eval_rougeLsum'], 7)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'summarization_no_trainer')))
       @slow
       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				List[str] 						=						F'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				int 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_bleu'], 30)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'epoch_0')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'translation_no_trainer')))
       @slow
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						logging.StreamHandler(sys.stdout)
           logger.addHandler(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Any 						=						F'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

           run_command(self._launch_args + testargs)
           _lowercase			:				str 						=						get_results(lowerCamelCase)
           self.assertGreaterEqual(result['eval_overall_accuracy'], 0.1_0)




       @mock.patch.dict(os.environ, {'WANDB_MODE': 'offline'})
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				str 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Optional[int] 						=						F'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

           if is_cuda_and_apex_available():
               testargs.append('--fp16')

           run_command(self._launch_args + testargs)
           _lowercase			:				List[str] 						=						get_results(lowerCamelCase)
           # The base model scores a 25%
           self.assertGreaterEqual(result['eval_accuracy'], 0.6)
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'step_1')))
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase, 'image_classification_no_trainer')))






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		'xmod'




			def __init__(				self   ,    snake_case=30522   ,    snake_case=768   ,    snake_case=12   ,    snake_case=12   ,    snake_case=3072   ,    snake_case="gelu"   ,    snake_case=0.1   ,    snake_case=0.1   ,    snake_case=512   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=1E-1_2   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    snake_case="absolute"   ,    snake_case=True   ,    snake_case=None   ,    snake_case=False   ,    snake_case=2   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=("en_XX",)   ,    snake_case=None   ,    **snake_case   ,    ):

						'''simple docstring'''


						super().__init__(pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    **snake_case  )

						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							hidden_size
						UpperCamelCase__       =							num_hidden_layers
						UpperCamelCase__       =							num_attention_heads
						UpperCamelCase__       =							hidden_act
						UpperCamelCase__       =							intermediate_size
						UpperCamelCase__       =							hidden_dropout_prob
						UpperCamelCase__       =							attention_probs_dropout_prob
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							type_vocab_size
						UpperCamelCase__       =							initializer_range
						UpperCamelCase__       =							layer_norm_eps
						UpperCamelCase__       =							position_embedding_type
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							classifier_dropout
						UpperCamelCase__       =							pre_norm
						UpperCamelCase__       =							adapter_reduction_factor
						UpperCamelCase__       =							adapter_layer_norm
						UpperCamelCase__       =							adapter_reuse_layer_norm
						UpperCamelCase__       =							ln_before_adapter
						UpperCamelCase__       =							list(snake_case  )
						UpperCamelCase__       =							default_language







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						if self.task == "multiple-choice":
									UpperCamelCase__       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
									UpperCamelCase__       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						    ]  )


